Gagan Agrawal

Artificial Intelligence · 2003–2018

A researcher in the Science corpus: 2 papers; trusts 66 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #10,761 of 35,345 by trust here, #27,551 by citations
• Optimization & Training — 26 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• ML Systems & Hardware — 11 citations into it
• Signal Processing — 9 citations into it; #1,930 of 7,572 by trust here, #5,570 by citations
• Computer Networks and Communications — 8 citations into it; #2,177 of 9,886 by trust here, #7,140 by citations
• Computational Mechanics — 5 citations into it

Whom Gagan Agrawal trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Kaiming He
3. Karen Simonyan
4. Xiangru Lian
5. Hang Su
6. Geoffrey E. Hinton
7. Andrew Zisserman
8. Yoshua Bengio
9. Pedro M. Domingos
10. Yann LeCun

Who trusts Gagan Agrawal overall

1. Hao Yu
2. Dmitrii Avdiukhin
3. Plamen Parvanov Angelov
4. Hanlin Tang
5. Ahmed Khaled
6. Elena Ikonomovska
7. András A. Benczúr
8. Mohamed Medhat Gaber
9. Leszek Rutkowski
10. Jihong Park

Papers from Gagan Agrawal's perspective

Ordered by how strongly the researchers Gagan Agrawal trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Learning Multiple Layers of Features from Tiny Images
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication (2018) — 205 citations
• Efficient decision tree construction on streaming data (2003) — 184 citations